This article considers sparse signal recovery in the presence of noise. A mutual incoherence condition which was previously used for exact recovery in the noiseless case is shown t...
Abstract--In this paper, we present a concise and coherent analysis of the constrained `1 minimization method for stable recovering of high-dimensional sparse signals both in the n...
—In this paper, we introduce a sparse approximation property of order s for a measurement matrix A: xs 2 ≤ D Ax 2 + β σs(x) √ s for all x, where xs is the best s-sparse app...
In this paper, we examine the problem of overcomplete representations and provide new insights into the problem of stable recovery of sparse solutions in noisy environments. We es...
The two major approaches to sparse recovery are L1-minimization and greedy methods. Recently, Needell and Vershynin developed Regularized Orthogonal Matching Pursuit (ROMP) that ha...